
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "91"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 123 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 118 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	37.391296	6.109559	59.861635	73.587213	20.885616	26.009062	33.634397	28.256289	0.0444	0.0452	-0.0008	1.551322	1.893637
2459593	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	30.861723	16.111091	71.227954	77.343747	30.430312	36.705212	21.952166	11.013067	0.0415	0.0442	0.0007	1.385926	1.716133
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.642557	1.574315	2.036821	2.457319	1.731851	2.382694	3.043306	0.558974	0.0288	0.0317	0.0035	nan	nan
2459591	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	29.258511	18.734725	84.170554	89.587042	26.708467	32.222168	11.335992	1.758790	0.0451	0.0446	-0.0004	1.500952	1.808824
2459590	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	34.977204	21.958651	78.150056	83.672731	28.304928	33.270608	16.072246	3.477414	0.0489	0.0494	-0.0008	1.671880	2.379089
2459589	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	32.511692	19.444551	72.877861	77.569889	16.757472	21.584476	22.204331	16.655839	0.0471	0.0495	-0.0006	1.537577	2.064488
2459588	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	197.467517	215.615325	inf	inf	23.159984	26.118622	58.351505	21.133113	nan	nan	nan	0.000000	0.000000
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	28.476600	21.478305	118.416894	126.729103	5.901119	7.446951	14.379905	10.525710	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	28.721627	17.203393	28.111323	29.479903	9.632444	11.097708	19.416180	5.693245	0.0418	0.0445	0.0009	1.305432	1.304357
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	158.918632	162.346974	inf	inf	31.284357	16.901643	523.575852	1764.627940	nan	nan	nan	0.000000	0.000000
2459584	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	27.345574	27.030089	96.899958	101.726675	15.869228	18.252015	23.827722	24.032021	0.0421	0.0425	0.0001	0.880280	0.877128
2459583	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.832098	17.943163	30.286507	31.720308	19.247929	22.948897	0.347089	0.246922	0.0427	0.0437	-0.0007	1.192971	1.178796
2459582	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.594418	15.770357	27.212893	28.834855	23.066685	27.363942	0.424216	0.436365	0.0400	0.0401	-0.0004	1.331998	1.614633
2459581	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.814200	21.445500	31.597896	32.461177	18.185690	22.238929	0.620073	0.509684	0.0444	0.0438	-0.0009	1.212589	1.203195
2459580	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.585031	16.292362	31.669111	33.274015	20.193678	23.368720	0.093754	0.296805	0.0456	0.0449	-0.0003	1.008618	0.995914
2459579	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.388140	16.106353	35.869387	37.834776	22.629953	26.710583	1.689168	1.729344	0.0415	0.0418	-0.0009	1.102047	1.099238
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	2.004546	1.823322	0.357411	0.354231	-0.341026	-0.425720	1.713104	1.642761	0.0277	0.0285	0.0009	nan	nan
2459577	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.472140	21.044684	29.325051	30.382989	20.406571	24.596100	0.485558	0.488482	0.0400	0.0412	-0.0013	1.270201	1.520605
2459576	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459575	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.841278	17.289047	23.110994	24.283923	15.308087	16.751700	7.300878	6.417480	0.0418	0.0428	-0.0012	1.372538	1.730403
2459574	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.737261	15.578846	22.172637	23.207824	20.292225	23.425062	1.398205	0.770106	0.0424	0.0425	-0.0013	1.450992	1.778855
2459573	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.394448	15.632339	38.136623	39.825012	24.109928	27.763452	1.283880	1.607301	0.0414	0.0424	-0.0004	1.340467	1.717117
2459572	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	16.257971	15.920075	22.261018	23.322848	15.496505	17.564306	2.803866	2.746875	0.0412	0.0410	-0.0009	1.317515	1.641483
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	nan	nan
2459570	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	22.586474	21.706498	23.043313	24.091032	1.212772	1.681812	3.986507	4.157351	0.0423	0.0464	-0.0008	1.014448	1.013990
2459569	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	20.729317	18.564653	29.454702	30.391578	10.656003	10.862976	8.343264	9.136942	0.0434	0.0451	-0.0008	1.323943	1.510693
2459566	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.673513	15.456349	29.104969	30.258250	18.596355	21.566717	-0.116059	-0.058993	0.0312	0.0355	0.0024	1.261935	1.260004
2459565	digital_ok	100.00%	-	-	-	100.00%	0.00%	4239.647163	4239.647163	3976.060084	3976.060084	-0.657802	-0.657802	-1.839007	-1.839007	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	14.186266	14.140813	30.949436	32.274542	2.264731	2.416258	0.925950	0.882367	0.0437	0.0424	-0.0003	1.333286	1.531874
2459563	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459562	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.465505	9.770058	15.181473	15.839361	2.331679	2.588513	0.799466	0.758925	0.0399	0.0425	0.0010	1.244974	1.373751
2459561	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	11.085066	10.339942	14.445159	15.060630	2.639024	2.903604	1.334730	1.134183	0.0420	0.0445	0.0017	1.274279	1.392793
2459560	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459559	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	12.895177	12.507867	16.227591	16.791414	2.588983	3.074863	1.113703	0.983981	0.0448	0.0463	0.0005	1.291087	1.453963
2459558	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	17.505385	16.697565	30.331511	32.084893	21.146873	23.132554	1.671459	1.409521	0.0398	0.0424	0.0004	1.199045	1.198782
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0276	0.0301	0.0013	nan	nan
2459556	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	18.094358	16.696984	32.778469	34.710267	19.012886	20.889837	3.322212	3.407620	0.0432	0.0437	-0.0008	1.268789	1.287176
2459553	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0449	0.0449	-0.0007	nan	nan
2459552	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459551	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	10.262052	9.797091	17.944415	18.602631	3.176816	4.254577	1.310351	1.125081	0.0407	0.0408	-0.0008	1.340956	1.509558
2459550	digital_ok	-	98.35%	98.35%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0466	0.0466	0.0005	nan	nan
2459549	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	15.404746	15.170193	33.265221	34.575858	20.529558	23.064741	1.313422	1.264078	0.0382	0.0407	-0.0003	1.268505	1.615704
2459542	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	21.858726	20.843684	67.885763	70.259092	1.085160	1.921049	1.607532	1.507157	0.0454	0.0462	0.0009	1.312393	1.298875
2459541	digital_ok	100.00%	94.18%	94.18%	0.00%	100.00%	0.00%	5.345060	4.726874	26.770184	27.587299	190.693646	203.330079	20.028256	27.988750	0.0919	0.0985	0.0070	1.389715	1.890229
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.00%	-0.240638	-0.812569	-0.842747	-0.695465	-0.687106	0.627845	-0.676914	-0.615667	0.7668	0.7618	0.3326	1.523845	1.455836
2459536	digital_ok	0.00%	0.00%	0.00%	0.00%	5.93%	0.00%	0.146919	-1.623046	-0.549325	-0.420313	-0.394078	-0.997034	0.014831	-1.312084	0.7354	0.7340	0.4159	1.328184	1.279182
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	-0.336824	-0.810493	-0.623766	-0.694182	-0.253149	0.941132	-0.458563	-0.567686	0.7912	0.7677	0.3584	1.839039	1.641966
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	-0.210346	-0.919521	-0.630271	-0.799302	0.458899	1.019833	-0.299261	-0.518161	0.7757	0.7585	0.3632	1.561907	1.478305
2459533	digital_ok	0.00%	0.00%	0.00%	0.00%	4.89%	0.00%	-0.063368	-1.158248	0.043554	-0.026124	-0.500021	-0.323727	-0.870361	-0.691643	0.7268	0.7202	0.4007	1.622415	1.478321
2459532	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	4.73%	0.00%	-0.682111	-0.790569	-0.528256	-0.504811	-0.494233	-0.408250	-0.108229	-0.296349	0.7470	0.7331	0.3958	1.974438	1.710751
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.492920	-0.751193	-0.009246	-0.061882	-1.366851	-0.790907	-0.894372	-0.715908	0.9142	0.9449	0.2716	0.000000	0.000000
2459523	digital_ok	0.00%	-	-	-	22.45%	0.00%	-0.728463	-1.839548	-0.750929	-0.433689	0.031869	-0.481462	-0.725537	-0.772127	nan	nan	nan	1.788268	1.681493
2459522	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.790190	-1.002056	-0.018811	-0.541150	-1.205657	0.336323	-0.338070	-0.590533	0.6875	0.7166	0.3278	1.531603	1.895999
2459513	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.601101	-0.722798	1.000705	0.770722	-0.529194	-0.270254	0.776870	0.467678	0.0316	0.0379	0.0028	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.295773	-0.146671	-0.520693	-0.995184	-1.297913	-0.711471	-0.620676	-0.800715	0.9207	0.9556	0.1652	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	-0.173833	-0.442742	-0.490049	0.548724	-1.487571	0.003238	-0.955373	-0.538627	0.8955	0.8750	0.1753	3.002823	2.809674
2459503	digital_ok	0.00%	0.00%	0.00%	0.00%	89.47%	0.00%	-0.173620	-0.784964	-0.473653	-0.080466	-0.932687	-0.107908	-0.716739	-0.528566	0.8816	0.8746	0.4267	0.000000	0.000000
2459501	digital_ok	0.00%	0.00%	0.00%	0.00%	76.74%	0.00%	0.748687	-0.534797	0.339381	-0.983839	-0.772515	0.299628	-0.821572	-0.925204	0.8333	0.8733	0.3604	0.000000	0.000000
2459500	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	10.655796	-0.691780	5.571028	-1.153379	16.369613	-0.578865	14.606820	2.852141	0.8031	0.8890	0.4292	0.000000	0.000000
2459499	digital_ok	0.00%	0.00%	0.00%	0.00%	97.37%	0.00%	0.108139	-0.118359	-0.176946	0.842836	-0.380453	1.555887	-0.708518	0.076805	0.8741	0.9331	0.1832	0.000000	0.000000
2459498	digital_ok	0.00%	0.00%	0.00%	0.00%	94.74%	0.00%	0.374732	-0.369245	-0.000005	0.871504	-0.279070	1.321070	-0.836447	-0.056032	0.8729	0.8931	0.3905	0.000000	0.000000
2459497	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.793059	0.427587	0.174285	1.337832	-0.403945	1.912491	-0.800232	-0.016416	0.1257	0.0705	0.0170	0.000000	0.000000
2459496	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.105820	-0.441850	-0.180993	0.985787	-0.942064	1.898998	-0.880710	-0.368830	0.1140	0.0751	0.0154	0.000000	0.000000
2459495	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.353416	-0.849403	-0.308262	0.636636	-0.894988	1.337185	-0.656431	-0.371932	0.1243	0.0729	0.0157	0.000000	0.000000
2459494	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.276760	-0.498999	-0.027874	0.641929	-1.007387	1.732800	-0.864950	-0.310083	0.1167	0.0949	0.0191	0.000000	0.000000
2459492	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.103405	-0.198223	-0.370952	2.379940	-1.315925	0.833413	-0.572398	0.043863	0.1357	0.1298	0.0264	0.000000	0.000000
2459491	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.251097	0.451727	-0.581415	3.477770	-0.851989	1.404296	-0.259265	0.814276	0.1215	0.1269	0.0217	0.000000	0.000000
2459490	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	0.022305	0.650934	-1.032816	3.335867	-1.235254	1.144433	-0.098035	0.928465	0.1261	0.1280	0.0277	0.000000	0.000000
2459489	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.520000	-0.205746	-0.690876	3.157785	-0.750321	3.076203	0.958147	0.675340	0.1386	0.1196	0.0242	0.000000	0.000000
2459488	digital_ok	0.00%	100.00%	100.00%	0.00%	100.00%	0.00%	-0.392611	-0.144800	-0.431637	2.556920	-0.915978	1.681050	-0.193235	1.048168	0.1339	0.1244	0.0261	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 91 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 91, Node 9:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶